Total Extent Research Articles

Effects of pirfenidone on scar size and ventricular remodeling after myocardial infarction: a preclinical study.

An intense fibrotic response after myocardial infarction (MI) may lead to scar expansion and left ventricular (LV) remodeling. We investigated the effects of the antifibrotic drug pirfenidone in this setting. Male Wistar rats were randomized to: sham procedure (n = 13), reperfused MI-induced by ligating the left anterior descending artery (LAD) for 45 min (n = 17), reperfused MI plus standard therapy (aspirin, angiotensin-converting enzyme inhibitor, beta blocker, and mineralocorticoid receptor antagonist) (n = 17), reperfused MI plus pirfenidone alone (n = 17), or reperfused MI plus standard therapy and pirfenidone (n = 17). Rats surviving MI induction underwent cardiac magnetic resonance scans after 72 h and 30 days from MI, and were sacrificed on day 31. Rats completing the whole protocol numbered 11 in the sham group, 9 in the untreated MI group, 8 in the standard treatment group, 9 in the pirfenidone alone group, and 9 in the standard treatment plus pirfenidone group. No significant differences emerged between LV volumes, ejection fraction or mass at 30 days or the differences from 72 h to 30 days. Small, nonsignificant differences between rats on pirfenidone alone vs. those on standard therapy emerged. The total extent of LV fibrosis, quantified as area and percentage of the tissue sample, did not differ significantly between rats on pirfenidone alone vs. those on standard therapy alone. Pirfenidone does not have additional effects on LV remodeling or fibrosis compared with standard therapy, but its effects are similar to standard therapy alone.

Numerical Investigation into Characteristics of WIPCC and Distortion of S-shaped Intake

Water injection pre-compressor cooling technology can efficiently increase the engine thrust and expand the flight envelope of the aircraft. Changes in the curvature of the intake and the evaporative cooling of the spray medium naturally distort the total pressure, total temperature, and characteristics of the swirl of the flow before it enters the engine. This study conducts numerical research on the aerothermal process of the WIPCC in an S-shaped intake using the Eulerian-Lagrangian method. The spatial evolution of critical variables along the flow direction and specific cross-sections were obtained by analyzing the statistical time-averaged results of multiphase flow. The transient distortion descriptors were quantified and analyzed following the SAE standard, which revealed the characteristics of various distortions and their spatial-temporal changes. Furthermore, the variables reflecting the transient flow structure were decomposed using the POD method to obtain the coherent structure of each mode and its frequency and energy characteristics. The results showed that aerodynamic breakup was the main cause of droplet size reduction and led to a decrease of 74.4% and 63.8% in Sauter droplet size under two conditions, respectively. The downstream mixing of flows in the S-duct mitigated the distortions caused by the intake's changing curvature and droplet evaporation. After the mixing of flows under specific conditions, the total pressure distortion coefficient decreased from 0.30 to 0.12, the swirl intensity decreased from 4°-6° to 2°-4°, and the high total temperature extent decreased from 0.65 to 0.35. The POD mode of the variables exhibited broadband characteristics with multiple spikes and energy dispersion. The main modes of the drop coefficients in total pressure and temperature corresponded to large- and small-scale structures, respectively, with Strouhal number ranges of 0.079-0.394 and 0.362-1.260. These results reveal the effects of WIPCC on the aerothermal processes in the S-shaped intake and the mechanism of generation and evolution of distortions. Moreover, the results provide reasonable boundary conditions for numerical investigations of wet compression under intake distortions.

FAST-ASKAP Synergy: Quantifying Coexistent Tidal and Ram Pressure Strippings in the NGC 4636 Group

Combining new H i data from a synergetic survey of Australian Square Kilometre Array Pathfinder (ASKAP) Widefield ASKAP L-band Legacy All-sky Blind surveY and Five-hundred-meter Aperture Spherical radio Telescope with the Arecibo Legacy Fast ALFA data, we study the effect of ram pressure and tidal interactions in the NGC 4636 group. We develop two parameters to quantify and disentangle these two effects on gas stripping in H i-bearing galaxies: the strength of external forces at the optical-disk edge, and the outside-in extents of H i-disk stripping. We find that gas stripping is widespread in this group, affecting 80% of H i-detected nonmerging galaxies, and that 41% are experiencing both types of stripping. Among the galaxies experiencing both effects, the two types of strengths are independent, while two H i-stripping extents moderately anticorrelate with each other. Both strengths are correlated with H i-disk shrinkage. The tidal strength is related to a rather uniform reddening of low-mass galaxies (M * < 109 M ☉) when tidal stripping is the dominating effect. In contrast, ram pressure is not clearly linked to the color-changing patterns of galaxies in the group. Combining these two stripping extents, we estimate the total stripping extent, and put forward an empirical model that can describe the decrease of H i richness as galaxies fall toward the group center. The stripping timescale we derived decreases with distance to the center, from ∼1 Gyr beyond R 200 to ≲10 Myr near the center. Gas depletion happens ∼3 Gyr since crossing 2R 200 for H i-rich galaxies, but much quicker for H i-poor ones. Our results quantify in a physically motivated way the details and processes of environmental-effects-driven galaxy evolution, and might assist in analyzing hydrodynamic simulations in an observational way.

Influence of Various Nanomaterials on the Rheology and Hydration Kinetics of Oil Well Cement.

Nanomaterials have great potential to influence the properties of cement-based materials due to their small particle size and large specific surface area. The influences of Nano-SiO2 (NS), gamma-nano-Al2O3 (GNA), alpha-nano-Al2O3 (ANA), and nano-TiO2 (NT) on the rheology and hydration kinetics of class G cement at 30 °C were investigated in this study. The nanomaterials were added in dry powder form at dosages of 1, 2, 3, 5, and 7% by weight of cement (bwoc), and their dispersion was accomplished using polycarboxylate superplasticizer (PCE) at a dosage of 1.6% bwoc. PCE provides a uniform dispersion of nanoparticles in the cement matrix, enhancing the efficiency of nanomaterials. The w/c ratio varied between 0.718 and 0.78 to form a constant-density slurry of 1.65 g/cm3. Our test results showed that NS and GNA caused significant increases in the rheology of the cement slurry, with this effect increasing with dosage, while ANA and NT tended to reduce the rheology of the slurry. Compared to a well-suspended and well-dispersed cement slurry generated by the use of PCE and diutan gum, all nanomaterials can accelerate early hydration by reducing the induction time, with GNA having the strongest influence, while NS was the only nanomaterial that further increased the long-term hydration heat release at 7 days. The stronger effect of NS and GNA on the cement slurry properties can be attributed to their higher chemical reactivity. The dosage effect on total hydration extent was relatively strong for ANA, NT, and NS from 3% to 5% but weak for GNA in the range from 3% to 7%.

Multi-objective numerical optimization of 3D-printed polylactic acid bio-metamaterial based on topology, filling pattern, and infill density via fatigue lifetime and mass.

Infill parameters are significant with regard to the overall cost and saving material while printing a 3D model. When it comes to printing time, we can decrease the printing time by altering the infill, which also reduces the total process extent. Choosing the right filling parameters affects the strength of the printed model. In this research, the effect of filling density and infill pattern on the fatigue lifetime of cylindrical polylactic acid (PLA) samples was investigated with finite element modeling and analysis. This causes the lattice structure to be considered macro-scale porosity in the additive manufacturing process. Due to the need for multi-objective optimization of several functions at the same time and the inevitable sacrifice of other objectives, the decision was to obtain a set of compromise solutions according to the Pareto-optimal solution technique or the Pareto non-inferior solution approach. As a result, a horizontally printed rectangular pattern with 60% filling was preferred over the four patterns including honeycomb, triangular, regular octagon, and irregular octagon by considering the sum of mass changes and fatigue lifetime changes, and distance from the optimal point, which is the lightest structure with the maximum fatigue lifetime as an objective function with an emphasis on mass as an important parameter in designing scaffolds and biomedical structures. A new structure was also proposed by performing a structural optimization process using computer-aided design tools and also, computer-aided engineering software by Dassault systems. Finally, the selected samples were printed and their 3D printing quality was investigated using field emission scanning electron microscopy inspection.

Expansive, positive changes to fish habitat diversity following the formation of a valley plug in a degraded desert river

AbstractWidespread hydrologic alterations have simplified in‐stream habitats in rivers globally, driving population declines and extirpations of many native fishes. Here, we examine how rapid geomorphic change in a historically degraded desert river has influenced habitat diversification and ecosystem persistence. In 2010, a large reach of the degraded and simplified lower San Rafael River (SRR), Utah, was impacted by the formation of a valley plug and began to shift from a homogenous, single‐thread channel to a complex, multi‐threaded riverscape. We combined field measurements and drone‐collected imagery to document changes in fish habitat due to the valley plug. Our results demonstrate that in 2021, the affected reach was more diverse than any other stream reach along the SRR, containing 641% more diverse habitat (e.g., pools, riffles, and backwaters) than what was measured in 2015. The plug reach also retained water for periods beyond what was expected during seasonal drying, with the total extent of inundation within the riverscape increasing by over 2800%. Since the formation of the valley plug, riparian habitat has increased by 230% and channel networks have expanded to more than 50 distinct channels throughout the zone of influence. Our results provide evidence of successful self‐restoration in a formerly highly degraded reach of desert river, and encourage new methods of desert river restoration. We aim to inform the use of large‐scale, disruptive restoration actions like intentional channel occlusions, with the goal of mitigating the impacts of simplification and increasing habitat persistence in the face of exacerbated aridity in the desert Southwest.

Plant-substrate biochar properties critical for mediating reductive debromination of 1,2-dibromoethane

Dibromoethane is a widespread, persistent organic pollutant. Biochars are known mediators of reductive dehalogenation by layered FeII-FeIII hydroxides (green rust), which can reduce 1,2-dibromoethane to innocuous bromide and ethylene. However, the critical characteristics that determine mediator functionality are lesser known. Fifteen biochar substrates were pyrolyzed at 600 °C and 800 °C, characterized by elemental analysis, X-ray photo spectrometry C and N surface speciation, X-ray powder diffraction, specific surface area analysis, and tested for mediation of reductive debromination of 1,2-dibromoethane by a green rust reductant under anoxic conditions. A statistical analysis was performed to determine the biochar properties, critical for debromination kinetics and total debromination extent. It was shown that selected plant based biochars can mediate debromination of 1,2-dibromoethane, that the highest first order rate constant was 0.082/hr, and the highest debromination extent was 27% in reactivity experiments with 0.1 µmol (20 µmol/L) 1,2-dibromoethane, ≈ 22 mmol/L FeIIGR, and 0.12 g/L soybean meal biochar (7 days). Contents of Ni, Zn, N, and P, and the relative contribution of quinone surface functional groups were significantly (p < 0.05) positively correlated with 1,2-dibromoethane debromination, while adsorption, specific surface area, and the relative contribution of pyridinic N oxide surface groups were significantly negatively correlated with debromination.

Habitat characteristics, population structure, distribution, and regeneration status of Taxus wallichiana in South Vietnam

Abstract. Hop NV, Xiong CC, Quy NV, Luong NT. 2023. Habitat characteristics, population structure, distribution, and regeneration status of Taxus wallichiana in South Vietnam. Nusantara Bioscience 15: 179-188. Taxus wallichiana Zucc. is an endangered plant species with high economic value, making it threatened to extinction. Biological information about this species is therefore essential for its conservation. This study is aimed to assess the habitat characteristics, population structure, distribution, and regeneration status of T. wallichiana in South Vietnam. Vegetation surveys were conducted from 2016 to 2021 in three regions in Lam Dong Province. The study revealed that it was distributed in regions with monsoon climate with a total extent of ??379.57 ha, with habitats located on the hillside, narrow shelves, dissected terrain, high slope, on feralite soil with light brown to yellow, red, the soil layer thickness over 0.3 m and in clusters or streaks. The study recorded 634 trees with a diameter at breast height (DBH)?25 cm and 137 trees with 6 cm ?DBH&lt;25 cm. The trees were found primarily at 1401-1500 m above sea level in a mixed broadleaved-coniferous forest. The growth indicators of DBH and overall height (Hvn) developed strongly, but the stand volume was low owing to low tree density. The exponential function was the most suitable for simulating the relationship between Hvn and DBH. Seven thousand six hundred and seventy-three regenerating young individuals were recorded with low density. The density of the regenerating trees decreased as the tree height increased. The finding of this study suggests that T. wallichiana faces the danger of extinction, so solutions are required to conserve and develop this threatened species.

Matrilineal phylogeny and habitat suitability of the endangered spotted pond turtle (Geoclemys hamiltonii; Testudines: Geoemydidae): a two-dimensional approach to forecasting future conservation consequences.

The spotted pond turtle (Geoclemys hamiltonii) is a threatened and less explored species endemic to Bangladesh, India, Nepal, and Pakistan. To infer structural variation and matrilineal phylogenetic interpretation, the present research decoded the mitogenome of G. hamiltonii (16,509 bp) using next-generation sequencing technology. The mitogenome comprises 13 protein-coding genes (PCGs), 22 transfer RNAs (tRNAs), two ribosomal RNAs (rRNAs), and one AT-rich control region (CR) with similar strand symmetry in vertebrates. The ATG was identified as a start codon in most of the PCGs except Cytochrome oxidase subunit 1 (cox1), which started with the GTG codon. The non-coding CR of G. hamiltonii was determined to have a unique structure and variation in different domains and stem-loop secondary structure as compared with other Batagurinae species. The PCGs-based Bayesian phylogeny inferred strong monophyletic support for all Batagurinae species and confirmed the sister relationship of G. hamiltonii with Pangshura and Batagur taxa. We recommend generating more mitogenomic data for other Batagurinae species to confirm their population structure and evolutionary relationships. In addition, the present study aims to infer the habitat suitability and habitat quality of G. hamiltonii in its global distribution, both in the present and future climatic scenarios. We identify that only 58,542 km2 (7.16%) of the total range extent (817,341 km2) is suitable for this species, along with the fragmented habitats in both the eastern and western ranges. Comparative habitat quality assessment suggests the level of patch shape in the western range is higher (71.3%) compared to the eastern range. Our results suggest a massive decline of approximately 65.73% to 70.31% and 70.53% to 75.30% under ssp245 and ssp585 future scenarios, respectively, for the years between 2021-2040 and 2061-2080 compared with the current distribution. The present study indicates that proper conservation management requires greater attention to the causes and solutions to the fragmented distribution and safeguarding of this endangered species in the Indus, Ganges, and Brahmaputra (IGB) river basins.

Machine Learning Helps Customize Spacing for Maximum Acreage Value

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper URTeC 3723023, “Using Machine Learning To Customize Development Unit Spacing for Maximum Acreage Value,” by M. Maguire, SPE, Diamondback Energy; A. Cui, Novi Labs; and T.E. Witham, Diamondback Energy, et al. The paper has not been peer reviewed. _ In the complete paper, machine-learning (ML) models were trained using geologic, completion, and spacing parameters to predict production across the primary developed formations in the Midland Basin. The approach of using ML to test several different combinations of spacing and completion designs can be repeated across a basin to find an economical, customized solution for each development unit. Introduction In contrast to conventional methods, ML offers a data-driven approach that can leverage the large amount of data generated by operators within unconventional plays. Several characteristics of ML models make them well-suited for spacing optimization, including the following: - ML models rely on statistical methods to establish relationships between the input variables and the output variables. - ML handles nonlinear relationships well. - Complex variable interactions can be difficult to understand with traditional methods. The downside is the time required to assemble the input data used for training an ML model. However, once a model is built that has an acceptable level of error along with data examples covering the range of cases to be evaluated, many alternative development scenarios can be quickly evaluated. In addition, with the right variables (features) included, a model could be applicable to an area much larger than that of pilot wells. ML Methods Each ML model used production data (monthly or daily format), directional survey data (to locate the wells and allow for spacing calculations), a header table with a variety of information on all the wells (i.e., completion information, formation, and completed lateral length), and grid data (typically geology data, such as effective porosity). For all models, private data, such as daily production data and detailed completion data, had precedence over data acquired from public sources or a third-party data provider. For a well to be included in the ML model, it generally had to have all data values populated; wells with missing data were excluded. Once the data were gathered, the derivative variables were calculated, along with several spacing parameters. For ML studies of unconventional developments, practitioners face two main subsurface questions: how to subdivide the formations to represent the drainage heights and which rock properties to pass to the model. Drainage heights defined by the operator are based on the geochemical typing data. These heights generally extended further than traditionally defined formations (Fig. 1). Because these drainage boundaries are defined by the true vertical depth (TVD) positions of specific formational tops, the total vertical extent of these drainage heights mirrors the geospatial variation in the TVD positions of their boundary tops. To generate the grids, geologic attributes were calculated from well logs. Log processing began after drainage heights were determined across the Midland Basin. Attributes with high correlation were selectively removed from the model inputs.

The impact of short-duration precipitation events over the historic Cauvery basin: a study on altered water resource patterns and associated threats

The Cauvery Delta, the ‘Rice Bowl’ of India follows a time-tested cultivation pattern over several irrigation zones. However, in this era of the Anthropocene, it is now well-established that short-duration, intense precipitation episodes will batter the flood plains year after year. The purpose of this first study is thus to quantify the impacts that such episodes may have on the floodplains of the Cauvery Delta and the concomitant threats to the historic Kallanai Dam. Precipitation events during the North-East monsoon period are driven not just by warm rain microphysics but also by large frozen hydrometeors falling from deep clouds causing undesirable flooding over the region to the extent of 66%. Additionally, from an assessment of the velocity heads and the floodwater depths, this study projects a heightened vulnerability. The total extent of submergence along riverbanks and other flow paths was estimated to be 145.98 {mathrm{km}}^{2} out of which 65.14% of the submerged area is agricultural land. The most important conceptual advance established in this paper is that sub-zones in major watersheds that are currently safe will get inundated in the RCP8.5 warming scenario in 2050.

Modelling the Effect of Water Removal by Reverse Osmosis on the Distillation of Mixtures of Short-Chain Organic Acids from Anaerobic Fermentation

Anaerobic fermentation (AF) to produce sustainable short-chain organic acids (SCOAs) has found no commercial application so far. This is due to several limitations, including the high energy consumption of the SCOAs’ separation from water by distillation. This study used AspenPlus simulations to investigate the benefits of reverse osmosis (RO) to remove water and concentrate the SCOAs from AF before their separation by distillation. The effect of RO on distillation reflux ratio, heat energy requirements, column diameter and equipment costs was simulated for the processing of model SCOA-containing streams, representing AF effluents. A total of 90 simulations were carried out, investigating three different SCOA compositions, corresponding to different ratios of lactic, acetic and propionic acids, three different concentrations of the total SCOAs (10, 50, 100 g/kg in the stream entering RO) and different extents of water removal by RO. RO brought a reduction in the distillation reboilers’ duty of up to more than 90%, with a reduction of column diameter of up to more than 70%. The total energy consumption, equipment cost and NPV (net present value) of the RO plus distillation process were in all cases more favourable than for the process without membranes.

Invasion in the Niger Delta: remote sensing of mangrove conversion to invasive Nypa fruticans from 2015 to 2020

AbstractInvasive species are a leading threat to biodiversity worldwide. Nypa palm (Nypa fruticans) has emerged as the predominant invasive species in the Niger Delta region of Nigeria. While endemic mangroves have high rates of carbon sequestration, stabilize coastlines and protect biodiversity, Nypa does not provide these services outside its native region of Southeast Asia. Oil exploration and urbanization in this region also exacerbate mangrove loss and Nypa spread. As Nypa is difficult to distinguish from endemic mangrove species in remotely sensed data, estimates of mangrove and ecosystem services losses in Nigeria are highly uncertain. Here, we analyse multisensor satellite data with machine learning to quantify the rapid expansion of Nypa from 2015 to 2020 in Nigeria. Using Landsat imagery and random forest classification, we quantify total potential Nypa extent in Nigeria in 2019. We then produced a Nypa extent map using iterative combinations of Sentinel‐1 SAR, Sentinel‐2 MSI and ALOS PALSAR. Random forest classifications using SAR data from ALOS and Sentinel‐1 were best suited for mapping Nypa extent with similar accuracies (78% and 75%, respectively). Based on data availability and accuracy, we focussed our change analysis on Sentinel‐1 SAR. Our results show ~28 000 ha of mangroves were converted to Nypa in Nigeria by 2020 and covered a larger extent than endemic mangroves, compounding the effect of the existing degradation and deforestation in the region. We also compared forest height and complexity estimates from Global Ecosystem Dynamics Investigation LiDAR to further distinguish between endemic mangroves and Nypa in three dimensions. Nypa structural variability, measured by top‐of‐canopy height, vegetation cover, plant area index, and foliage height diversity, was lower than that of mangroves. At current rates of Nypa expansion, the entire area of study would be invaded by Nypa by 2028, with potentially detrimental consequences to the ecosystem services provided by mangroves.

Climatology of Tropical Cyclone Rainfall Magnitude at Different Landfalling Stages: An Emphasis on After-Landfall Rain

Abstract Estimating the magnitude of tropical cyclone (TC) rainfall at different landfalling stages is an important aspect of the TC forecast that directly affects the level of response from emergency managers. In this study, a climatology of the TC rainfall magnitude as a function of the location of the TC centers within distance intervals from the coast and the percentage of the raining area over the land is presented on a global scale. A total of 1834 TCs in the period from 2000 until 2019 are analyzed using satellite information to characterize the precipitation magnitude, volumetric rain, rainfall area, and axial-symmetric properties within the proposed landfalling categories, with an emphasis on the postlandfall stages. We found that TCs experience rainfall maxima in regions adjacent to the coast when more than 50% of their rainfall area is over the water. TC rainfall is also analyzed over the entire TC extent and the portion over land. When the total extent is considered, rainfall intensity, volumetric rain, and rainfall area increase with wind speed intensity. However, once it is quantified over the land only, we found that rainfall intensity exhibits a nearly perfect inversely proportional relation with the increase in TC rainfall area. In addition, when a TC with life maximum intensity of a major hurricane makes landfall as a tropical depression or tropical storm, it usually produces the largest spatial extent and the highest volumetric rain. Significant Statement This study aims to describe the cycle of tropical cyclone (TC) precipitation magnitude through a new approach that defines the landfall categories as a function of the percentage of the TC precipitating area over the land and ocean, along with the location of the TC centers within distance intervals from the coast. Our central hypothesis is that TC rainfall should exhibit distinct features in the long-term satellite time series for each of the proposed stages. We particularly focused on the overland events due to their effects on human activities, finding that the TCs that at some point of their life cycle reached major hurricane strength and made landfall as a tropical storm or tropical depression produced the highest volumetric rain over the land surface. This research also presents key observational evidence of the relationship between the rain rate, raining area, and volumetric rain for landfalling TCs.

Accuracy Improvement Based on Classic Neural Network: Voting, Restarting and Quantization

Since AlphaGo beat the world Go champion in 2016, which attracted wide attention, the neural network has become more and more popular in recent years, and people’s research on it has gradually improved and been applied in different fields. Today, artificial intelligence and machine learning have become an essential part of modern society and intelligent systems. We do image recognition, speech recognition, and visual learning, closely related to machine learning. However, in machine learning, unsatisfactory training results or even training failure is always encountered. Therefore, in machine learning, it is imperative to improve the accuracy of neural network training results. In this paper, speech recognition, image processing, MNIST, and other classical neural network models will be used to set the training parameters of the neural network better and improve the accuracy of training through voting, quantization, restart, and other methods. The part of research is aiming to find the relationship between restart numbers on the training process and the total extent of learning improvement. At the same time, several algorithms on utilizing these restart numbers are to be compared and selected. Finally, the conclusion is drawn that the more restart made in the training process with a convolutional neural network, the less profit on accuracy improvement we gain from restarting the process.

Satellite data reveal how Sudd wetland dynamics are linked with globally-significant methane emissions

Recent work has highlighted the large role of methane emissions from the Sudd wetland and surrounding ecosystems on the global atmospheric growth rate of methane since 2010. These emissions are driven by high rainfall over basin catchments linked with the positive phase of the Indian Ocean Dipole. We reconstruct flood inundation for the Sudd wetland over a 38-year period at a spatial resolution of 30 m using a new satellite Earth Observation (EO) wetland mapping tool. We reveal considerable changes in the wet season extent of the wetland, including an increase >300% since 2019 compared to the median 1984–2022 extent. We report major increases in flood extent within grassland-dominated floodplains outside of the area currently defined Sudd wetland region. These year-to-year changes in wetland extent are corroborated with total water storage anomalies inferred from satellite data (Pearson correlation R = 0.92), Lake Victoria levels (R = 0.73), and anomalies in reported annual mean global methane growth rates since 2009 (R = 0.88). Our analysis shows that flood water inundation is dominated by inundated vegetation and aquatic vegetation, accounting for an average of 40% and 50% of total extent, respectively, compared to open water that accounted for just 9% of inundation in a typical year. This is consistent with recent studies that report wetland methane emissions are focused on areas with inundated vegetation. Our findings also support recent studies that highlight the significant role of the Sudd wetland in driving anomalously large global atmospheric annual growth rates, 2020–2022. By capturing high resolution information on inundated vegetation, our EO wetland mapping tool has significant potential for improved wetland emission estimates of methane. Vascular plants common in the Sudd wetland, e.g. macrophytes including Phragmites Australis and Cyperus Papyrus, seem to play a key role in methane emissions and we recommend they should be the focus of future research.

СВЯЗИ МЕЖГОДОВЫХ ИЗМЕНЕНИЙ ХАРАКТЕРИСТИК ЛЕДЯНОГО ПОКРОВА И УРОВНЕЙ В ЗАЛИВАХ КАРСКОГО МОРЯ ДЛЯ ЗИМНИХ МЕСЯЦЕВ

The paper aims to put forward and check the hypothesis that interannual variations in the monthly means of sea ice thickness and concentration in the Kara Sea gulfs from November to March can be significantly influenced by variations in their sea levels. Some areas of the Gulf of Ob, Yenisei, Baidarata and Gydan gulfs are considered. The areas where such influence was really observed in 1993-2019 are identified. It is suggested that with further warming of the Arctic climate, the total extent of such areas will grow, and consideration of the revealed relationships will provide more precise prediction of changes in ice and navigation conditions in these gulfs.

Characterizing the 2020 summer floods in South China and effects on croplands

Floods occur more frequently in the context of climate change; however, flood monitoring capacity has not been well established. Here, we used a synergic mapping framework to characterize summer floods in the middle and lower reaches of the Yangtze River Plain and the effects on croplands in 2020, from both floodextent and intensity perspectives. We found that the total flood extent was 4936km2 from July to August, and for flood intensity, 1658, 1382, and 1896km2 of areas experienced triple, double, and single floods. A total of 2282km2 croplands (46% of the flooded area) were inundated mainly from Poyang and Dongting Lake Basins, containing a high ratio of moderate damage croplands (47%). The newly increased flooding extent in 2020 was 29% larger than the maximum ever-flooded extent in 2015-2019. This study is expected to provide a reference for rapid regional flood disaster assessment and serving mitigation.

Hydrological Dynamics of the Pantanal, a Large Tropical Floodplain in Brazil, Revealed by Analysis of Sentinel-2 Satellite Imagery

Extensive tropical floodplain wetlands, such as the Brazilian Pantanal, are complex ecosystems composed of mosaics of permanently and seasonally flooded habitats and are increasingly threatened by land use and climate change. Spatial and interannual variability in the seasonal flood pulse is a fundamental ecological driver in these ecosystems. This study analyzes optical imagery from the Sentinel-2 satellite to determine the extent and seasonal patterns of inundation over five years in the northern Pantanal, a Ramsar site renowned for its wildlife. The study site is bordered by the Cuiabá and São Lourenço rivers, each with distinct flow regimes. Inundation patterns were revealed with a combination of water indices, supervised classification of land cover, and a digital elevation model. Total extent of flooding was underestimated by the optical imagery, but open water bodies were readily delineated with the land cover classification.

Clinical course and risk factors for development and progression of interstitial lung disease in primary Sjögren’s syndrome

This single-center, retrospective study aimed to investigate the course and prognostic factors of patients with primary Sjögren syndrome-associated interstitial lung disease (pSS-ILD). We included 120 pSS patients who underwent at least two high-resolution computed tomography (HRCT) scans between 2013 and 2021. Clinical symptoms, laboratory data, HRCT findings, and pulmonary function test results were collected. Two thoracic radiologists reviewed the HRCT findings. In patients with pSS without ILD at baseline (n = 81), no development of ILD was found on follow-up (median, 2.8 years). In patients with pSS-ILD (n = 39), total disease extent, extent of coarse reticulation, and traction bronchiectasis increased on HRCT, whereas the extent of ground glass opacity (GGO) decreased at follow-up (median, 3.2 years) (each p < 0.001). In progressive group of pSS-ILD (48.7%), the extent of coarse reticulation and coarseness score of fibrosis were increased at follow-up (p < 0.05). Usual interstitial pneumonia pattern on CT (OR, 15.237) and follow-up duration (OR, 1.403) were independent risk factors for disease progression in patients with pSS-ILD. In both progressive and non-progressive pSS-ILD, GGO decreased, whereas the extent of fibrosis increased even after treatment with glucocorticoid and/or immunosuppressants. In conclusion, progression occurred in approximately half of the pSS-ILD patients with slow gradual deterioration. Our study identified a definite group of progressive pSS-ILD who did not respond to current anti-inflammatory treatment.